Course specification for CIS8008

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CIS8008 Business Intelligence

Semester 1, 2020 On-campus Springfield
Short Description: Business Intelligence
Units : 1
Faculty or Section : Faculty of Business, Education, Law and Arts
School or Department : School of Management and Enterprise
Student contribution band : Band 2
ASCED code : 020307 - Decision Support Systems
Grading basis : Graded

Staffing

Examiner:

Other requisites

Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at .

Rationale

It is important for all professions to develop a good understanding of business intelligence, current and emerging technology trends and how business intelligence can be applied to support data-driven decision-making. Students need to develop a good understanding of business intelligence and the underlying data warehouse and big data architecture, and how to apply data mining and data visualisation to support decision-making of organisations in order to achieve superior business performance management. Business intelligence plays a critical role in ensuring that organisations achieve strategic goals by monitoring organisational performance and achievement of day-to-day operational goals. It is appropriate to examine business intelligence in terms of theory, design, application, implementation and utilisation challenges and opportunities in organisations.

Synopsis

This course provides students with a thorough understanding of theory, design, implementation and application of business intelligence systems in an organisational context of decision making that is evidence based for enhanced business performance. Students will develop a good understanding of data driven decision making, data warehouse and big data architecture and how to apply business intelligence tools to analyse and present information to support improved decision making in organisations. Students will be assessed on their understanding of key concepts concerning the design implementation and use of business intelligence systems and application of business intelligence through data mining and data visualisation tools to help solve real world business problems. The architecture, implementation, and practical use of business intelligence are considered in current and real life contexts.

Objectives

On successful completion of this course, students should be able to:

  1. apply knowledge of people, markets, finances, technology and management in a global context of business intelligence practice (data warehousing and big data architecture, data mining process, data visualisation and performance management) and resulting organisational change and understand how these apply to the implementation of business intelligence in organisation systems and business processes;
  2. identify and solve complex organisational problems creatively and practically through the use of business intelligence and critically reflect on how evidence based decision making and sustainable business performance management can effectively address real-world problems;
  3. comprehend and address complex ethical dilemmas that arise from evidence based decision making and business performance management;
  4. communicate effectively in a clear and concise manner in written report style for senior management with the correct and appropriate acknowledgment of the main ideas presented and discussed.

Topics

Description Weighting(%)
1. Decision making and business intelligence 10.00
2. Business intelligence systems components and tools 10.00
3. Data warehousing and big data architecture 10.00
4. Data mining 30.00
5. Data visualisation 10.00
6. Business performance management 20.00
7. Business intelligence implementation/utilisation challenges and opportunities 10.00

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=01&subject1=CIS8008)

Please for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)

Sharda, R, Delen, D & Turban, E 2017, Business intelligence: analytics, and data science: a managerial perspective, 4th global edn, Pearson, Harlow, Essex.

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.
Jones, B 2014, Communicating data with tableau: designing, developing and delivering data visualizations, O'Reilly, Sebastopol, California.
(available as Kindle or in hard copy.)
Kimball, R & Ross, M 2013, The data warehouse toolkit: the complete guide to dimensional modeling, 3rd edn, John Wiley & Sons, New York.
(available to view online via USQ Library - note there are some restrictions on usage, such as no printing or limited printing of e-books.)
North, M 2012, Data mining for the masses, The Global Textbook Project.
(available at: .)

Student workload expectations

Activity Hours
Directed ¾«¶«´«Ã½app 36.00
Independent ¾«¶«´«Ã½app 129.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
ASST 1 - ONLINE QUIZ 100 5 20 Mar 2020
ASST 2 - WRITTEN REPORT 100 15 14 Apr 2020
ASST 3 - WRITTEN REPORT 100 40 18 May 2020
EXAMINATION 100 40 End S1 (see Examination notes below)

Examination
Description Marks out of Wtg (%) Notes
EXAM B (WRITTEN) 70 28 (see exam note 1)
MOODLE QUIZ 30 12 (see exam note 2)

Exam Notes
  1. This will be a take home exam. Students will be provided further instruction regarding the exam by their examiner via ¾«¶«´«Ã½appDesk. The examination date will be available via UConnect when the Alternate Assessment Schedule has been released.
  2. This will be an online exam. Students will be provided further instruction regarding the exam by their examiner via ¾«¶«´«Ã½appDesk.

Important assessment information

  1. Attendance requirements:
    Online: If you are an international student in Australia, you are advised to attend all classes at your campus. For all other students, there are no attendance requirements for this course. However, it is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

    On-campus: It is the students' responsibility to attend and participate appropriately in all activities (such as lectures, tutorials, laboratories and practical work) scheduled for them, and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    Due to COVID-19 the requirements for S1 2020 are: To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.

    Requirements after S1 2020;
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks. (Depending upon the requirements in Statement 4 below, students may not have to satisfactorily complete each assessment item to receive a passing grade in this course.)

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    Due to COVID-19 the requirements for S1 2020 are: To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.

    Requirements after S1 2020;
    To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    Due to COVID-19 the requirements for S1 2020 are: An Open Examination is one in which candidates may have access to any printed or written material and a calculator during the examination.

    Requirements after S1 2020;
    This is a restricted examination. The only materials that candidates may use in the examination for this course are:
    1. writing materials. These must be non-electronic and free from material which could give the student an unfair advantage in the examination.
    2. an unmarked non-electronic translation dictionary (but not technical dictionary). A student whose first language is not English may take a translation dictionary into the examination room. A translation dictionary with any handwritten notes will not be permitted. Translation dictionaries will be subject to perusal and may be removed from the candidate's possession until appropriate disciplinary action is completed if found to contain material that could give the candidate an unfair advantage.
    3. a calculator which cannot hold textual information (students must indicate on their examination paper the make and model of any calculator(s) they use during the examination).


  7. Examination period when Deferred/Supplementary examinations will be held:
    Due to COVID-19 the requirements for S1 2020 are: The details regarding deferred/supplementary examinations will be communicated at a later date

    Requirements after S1 2020;
    Any Deferred or Supplementary examinations for this course will be held during the next examination period.

  8. ¾«¶«´«Ã½app Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene ¾«¶«´«Ã½app policies and practices. These policies can be found at .

Assessment notes

  1. Referencing in assignments:
    Harvard (AGPS) is the referencing system required in this course. Students should use Harvard (AGPS) style in their assignments to format details of the information sources they have cited in their work. The Harvard (AGPS) style to be used is defined by the USQ Library's referencing guide at .

Date printed 19 June 2020